Structural Health Monitoring: Real-Time Data Analysis and Damage Detection

2019 ◽  
pp. 171-197
Author(s):  
Yavuz Kaya ◽  
Erdal Safak
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Real Time ◽  
Health Monitoring ◽  
Time Data ◽  
Structural Health ◽  
Real Time Data

A hybrid learning strategy for structural damage detection

2020 ◽  
pp. 147592172096694
Author(s):  
Lorena Andrade Nunes ◽  
Rafaelle Piazzaroli Finotti Amaral ◽  
Flávio de Souza Barbosa ◽  
Alexandre Abrahão Cury
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Real Time ◽  
Health Monitoring ◽  
Time Domain ◽  
Structural Damage ◽  
Machine Learning Techniques ◽  
Hybrid Classifier ◽  
Detection Techniques ◽  
Structural Health

Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised ( k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

scholarly journals The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring

Aerospace ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 64
Author(s):  
Sarah Malik ◽  
Rakeen Rouf ◽  
Krzysztof Mazur ◽  
Antonios Kontsos
Keyword(s):  
Structural Health Monitoring ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Health Monitoring ◽  
Sensor Data ◽  
Management Approach ◽  
Time Data ◽  
Data Quality Management ◽  
Structural Health

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.

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